Probabilistic Arithmetic Automata and Their Application to Pattern Matching Statistics
CPM '08 Proceedings of the 19th annual symposium on Combinatorial Pattern Matching
Bayesian unsupervised learning of DNA regulatory binding regions
Advances in Artificial Intelligence
Parallel Position Weight Matrices algorithms
Parallel Computing
Significant motifs in time series
Statistical Analysis and Data Mining
Efficient mining of correlated sequential patterns based on null hypothesis
Proceedings of the 2012 international workshop on Web-scale knowledge representation, retrieval and reasoning
Probabilistic Arithmetic Automata and Their Applications
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Motivation: Many heuristic algorithms have been designed to approximate P-values of DNA motifs described by position weight matrices, for evaluating their statistical significance. They often significantly deviate from the true P-value by orders of magnitude. Exact P-value computation is needed for ranking the motifs. Furthermore, surprisingly, the complexity of the problem is unknown. Results: We show the problem to be NP-hard, and present MotifRank, software based on dynamic programming, to calculate exact P-values of motifs. We define the exact P-value on a general and more precise model. Asymptotically, MotifRank is faster than the best exact P-value computing algorithm, and is in fact practical. Our experiments clearly demonstrate that MotifRank significantly improves the accuracy of existing approximation algorithms. Availability: MotifRank is available from http://bio.dlg.cn Contact: mzhang@cshl.edu mli@uwaterloo.ca Supplementary information: Supplementary data are available at Bioinformatics online.